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1,434 results for “subtypes”
Data from: Subtype diversity and reassortment potential for co-circulating avian influenza viruses at a diversity hot spot
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Memory reactivation in rat medial prefrontal cortex occurs in a subtype of cortical UP state during slow-wave sleep
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Data from: Aldosterone reduction rate after saline infusion may be a novel clinical prediction of determining subtypes of primary aldosteronism
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Molecular subtyping of alzheimer’s disease with consensus non-negative matrix factorization
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Genetically determined blood pressure, antihypertensive drug classes and risk of stroke subtypes
<p><b>Objective: </b>We employed Mendelian Randomization to explore whether the effects of blood pressure (BP) and BP lowering through different antihypertensive drug classes on stroke risk vary by stroke etiology.</p> <p><b>Methods: </b>We selected genetic variants associated with systolic and diastolic BP and BP-lowering variants in genes encoding antihypertensive drug targets from a GWAS on 757,601 individuals. Applying two-sample Mendelian randomization, we examined associations with any stroke (67,162 cases; 454,450 controls), ischemic stroke and its subtypes (large artery, cardioembolic, small vessel stroke), intracerebral hemorrhage (ICH, deep and lobar), and the related small vessel disease phenotype of WMH.</p> <p><b>Results</b>: Genetic predisposition to higher systolic and diastolic BP was associated with higher risk of any stroke, ischemic stroke, and ICH. We found associations between genetically determined BP and all ischemic stroke subtypes with a higher risk of large artery and small vessel stroke compared to cardioembolic stroke, as well as associations with deep, but not lobar ICH. Genetic proxies for calcium channel blockers, but not beta blockers, were associated with lower risk of any stroke and ischemic stroke. Proxies for CCBs showed particularly strong associations with small vessel stroke and the related radiological phenotype of WMH.</p> <p><b>Conclusions: </b>This study supports a causal role of hypertension in all major stroke subtypes except lobar ICH. We find differences in the effects of BP and BP lowering through antihypertensive drug classes between stroke subtypes and identify calcium channel blockade as a promising strategy for preventing manifestations of cerebral small vessel disease.</p>
Biological subtypes of Alzheimer's disease: a systematic review and meta-analysis
<p><span><b>Objective: </b>We conducted a systematic review and meta-analysis on subtype studies of Alzheimer's disease (AD) based on postmortem and neuroimaging data, with the ultimate goal of advancing our understanding of mechanisms driving heterogeneity in AD.</span></p> <p><span><b>Methods: </b>EMBASE, PubMed and Web of Science databases were consulted until July 2019.</span></p> <p><span><b>Results:</b> Neuropathology and neuroimaging studies have consistently identified three subtypes of AD based on the distribution of tau-related pathology and regional brain atrophy: typical, limbic-predominant, and hippocampal-sparing AD. A fourth subtype, minimal atrophy AD, has been identified in several neuroimaging studies. Typical AD displays tau-related pathology and atrophy both in hippocampus and association cortex, and has a pooled frequency of 55%. Limbic-predominant, hippocampal-sparing, and minimal atrophy AD had a pooled frequency of 21%, 17%, and 15%, respectively. Between-subtype differences were found in age at onset, age at assessment, sex distribution, years of education, global cognitive status, disease duration, APOE e4 genotype, and CSF biomarker levels.</span></p> <p><span><b>Conclusion:</b> We identified two core dimensions of heterogeneity: "typicality" and "severity". We propose that these two dimensions determine individuals' belonging to one of the AD subtypes based on the combination of protective factors, risk factors, and concomitant non-AD brain pathologies. This model is envisioned to aid with framing hypotheses, study design, results interpretation, and understanding mechanisms in future subtype studies. Unraveling the heterogeneity within AD is critical for implementing precision medicine approaches and for ultimately developing successful disease-modifying drugs for AD.</span></p>
Data from: Co-expression of two subtypes of melatonin receptor on rat M1-type intrinsically photosensitive retinal ganglion cells
Intrinsically photosensitive retinal ganglion cells (ipRGCs) are involved in circadian and other non-image forming visual responses. An open question is whether the activity of these neurons may also be under the regulation mediated by the neurohormone melatonin. In the present work, by double-staining immunohistochemical technique, we studied the expression of MT1 and MT2, two known subtypes of mammalian melatonin receptors, in rat ipRGCs. A single subset of retinal ganglion cells labeled by the specific antibody against melanopsin exhibited the morphology typical of M1-type ipRGCs. Immunoreactivity for both MT1 and MT2 receptors was clearly seen in the cytoplasm of all labeled ipRGCs, indicating that these two receptors were co-expressed in each of these neurons. Furthermore, labeling for both the receptors were found in neonatal M1 cells as early as the day of birth. It is therefore highly plausible that retinal melatonin may directly modulate the activity of ipRGCs, thus regulating non-image forming visual functions.
Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia
<p>This upload is associated with the software, <a href="https://github.com/pr4deepr/GutAnalysisToolbox">Gut Analysis Toolbox</a> (GAT).</p> <p>If you use it please cite:</p> <p><strong><em>Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: <a href="https://doi.org/10.1242/jcs.261950" target="_blank" rel="noopener">https://doi.org/10.1242/jcs.261950</a></em></strong></p> <p>The upload contains<strong> StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and FPN+ResNet101 model for enteric ganglia in 2D in gut wholemount tissue.</strong> GAT is implemented in Fiji, but the models can be used in any software that supports StarDist and the use of 2D UNet models. The files here also consist of <strong>Python notebooks (Google Colab)</strong>, training and test data as well as reports on model performance.</p> <p>Note: The enteric ganglia model is has been updated to v3 which uses pytorch and is a different architecture (FPN+ResNet101).</p> <p>The model files are located in the respective folders as zip files. The folders have also been zipped:</p> <ul> <li>Neuron (Hu; <a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_model_QA.zip</li> <li>StarDist Model File:2D_enteric_neuron_v4_1.zip </li> <li>DeepImageJ compatible model: 2D_enteric_neuron.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Neuronal subtype (<a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_subtype_model_QA.zip</li> <li>Model File: 2D_enteric_neuron_subtype_v4.zip</li> <li>DeepImageJ compatible model: 2D_enteric_neuron_subtype.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Enteric ganglia (2D FPN_ResNet101; Use in FIJI with <a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a>) <ul> <li>Main folder: 2D_enteric_ganglia_v3_training.zip</li> <li>Model File: 2D_Ganglia_RGB_v3.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> </ul> <p>For the all models, files included are:</p> <ol> <li>Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet.</li> <li>Training and Test datasets used for training.</li> <li>Google Colab notebooks used for training and quality assurance (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic notebooks</a>).</li> <li>Python notebook and code for training ganglia model with QA.</li> <li>Quality assurance reports generated from above notebooks.</li> <li>StarDist model exported for use in QuPath.</li> </ol> <p>The model files can be used within can be used within the software, <a href="https://github.com/stardist/stardist">StarDist</a>. They are intended to be used within FIJI or QuPath, but can be used in any software that supports the implementation of StarDist in 2D.</p> <p><strong>Data:</strong></p> <p>All the images were collected from 4 different research labs and a public database (<a href="https://sparc.science/data?type=dataset">SPARC database</a>) to account for variations in image acquisition, sample preparation and immunolabelling.</p> <p>For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue.</p> <p>For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used..</p> <p>25 images were used from the following entries in the <a href="https://sparc.science/data?type=dataset">SPARC database</a>:</p> <ul> <li><a href="https://doi.org/10.26275/9FFG-482D">Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. </a></li> <li><a href="https://doi.org/10.26275/PZEK-91WX">Graham, K. D., Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C. M., Feldman, M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E., Howard, M., & Heuckeroth, R. (2020). Robust 3-Dimensional visualization of human colon enteric nervous system without tissue sectioning (Version 1) [Data set]. SPARC Consortium.</a></li> <li>Wang, L., Yuan, P.-Q., Gould, T. and Tache, Y. (2021). Antibodies Tested in theColon – Mouse (Version 1) [Data set]. SPARC Consortium. doi:10.26275/i7dl-58h</li> </ul> <p>Additional images for new ganglia model:</p> <ul> <li>Hamnett, R., Dershowitz, L. B., Sampathkumar, V., Wang, Z., Gomez-Frittelli, J., De Andrade, V., Kasthuri, N., Druckmann, S. and Kaltschmidt, J. A. (2022b). Regional cytoarchitecture of the adult and developing mouse enteric nervous system. Curr. Biol. 32, 4483-4492.e5.</li> </ul> <p>The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using: </p> <ul> <li> <p>Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3) </p> </li> <li> <p>Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88) </p> </li> <li> <p>Zeiss Axio Imager M2 (20X HC PL APO NA 0.3) </p> </li> <li> <p>Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45) </p> </li> </ul> <p>Human tissue images were acquired using: </p> <ul> <li> <p>IX71 Olympus microscope (10X HC PL APO NA 0.3) </p> </li> </ul> <p>For more information, visit the <a href="https://gut-analysis-toolbox.gitbook.io/docs" target="_blank" rel="noopener">Documentation</a> website.</p> <p><strong>NOTE:</strong> The images for enteric neurons and neuronal subtypes have been rescaled to 0.568 µm/pixel for mouse and rat. For human neurons, it has been rescaled to 0.9 µm/pixel . This is to ensure the neuronal cell bodies have similar pixel area across images. The area of cells in pixels can vary based on resolution of image, magnification of objective used, animal species (larger animals -> larger neurons) and potentially how the tissue is stretched during wholemount preparation </p> <p>Average neuron area for neuronal model: 701.2 ± 195.9 pixel<sup>2 </sup>(Mean ± SD, 6267 cells)</p> <p>Average neuron area for neuronal subtype model: 880.9 ± 316 pixel<sup>2 </sup>(Mean ± SD, 924 cells)</p> <p><strong>Software References:</strong></p> <p><strong><a href="https://github.com/stardist/stardist">Stardist</a></strong></p> <p>Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em> (pp. 265-273). Springer, Cham.</p> <p><strong><a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a></strong></p> <p>Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A. and Sage, D., 2021. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. <em>Nature Methods</em>, <em>18</em>(10), pp.1192-1195.</p> <p><strong><a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic</a></strong></p> <p>von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. <em>Nature communications</em>, <em>12</em>(1), pp.1-18.</p>
Normalised FMA-UE scores at different timepoints, according to study and stroke subtype
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FMA-UE scores at different timepoints, according to study and stroke subtype
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Normalised FMA-UE scores at different timepoints, according to study and stroke subtype
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FMA-UE scores at different timepoints, according to study and stroke subtype
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Gene signatures of the Vanderbilt TNBC subtypes
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Association of poultry vaccination with interspecies transmission and molecular evolution of H5 subtype avian influenza virus
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Fetal/maternal-determined birth weight and adulthood type 2 diabetes and its subtypes: a Mendelian randomization study
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Kludt et al. Cell Reports Medicine - Challenging Subtyping Cases (Biopsy)
<p>Anonymized whole slide images to challenging biopsy cases from the publication:</p> <p>"Next generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms"</p> <p>in Cell Reports Medicine 2024</p> <p>The ground truth information is included.</p> <p><br>The dataset can be used for academic research purposes only.</p> <p> </p> <p>(c) Yuri Tolkach<br> </p>
Kludt et al. Cell Reports Medicine - Challenging Subtyping Cases (Resection)
<p>Anonymized whole slide images to challenging resection cases from the publication:</p> <p>"Next generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms"</p> <p>in Cell Reports Medicine 2024</p> <p>The ground truth information is included.</p> <p> </p> <p>The dataset can be used for academic research purposes only. </p> <p> </p> <p>(c) Yuri Tolkach</p>
Contrastive learning-based histopathological feature infers molecular subtypes and clinical outcomes of breast cancer from unannotated whole slide images
<p>The breast cancer cohort came from the Changzhou Second People's Hospital (CZSPH) in Jiangsu, China. This cohort collected 91 FFPE WSIs from 90 breast cancer patients, including 15 recurrence cases within 5 years.</p>
Relationship Between Cardiovascular Disease in Asthma and Eosinophil Subtypes and Biomarkers of Bronchial Remodeling.
ClinicalTrials.gov study NCT05589779. IPD Sharing: UNDECIDED. Countries: 0. Publications: 13.
Contrast-Enhanced Ultrasound for Kidney Cancer Subtyping and Staging
ClinicalTrials.gov study NCT04021238. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.